Oct 17, 2026

An AI Agent Got Its User Banned From Resy — Keycard, JPMorgan Chase & AAuth

A panel of experts discusses the challenges of granting autonomy to AI agents due to coarse-grained authorization systems. They advocate for intent-based runtime authorization, leveraging concepts like "missions" and "budgets" from AOTH, brain-hands architecture, and dynamic policy management. The discussion covers agentic commerce, accountability through tamper-resistant audit logs, and whether P(doom) is a real threat, emphasizing engineering ingenuity and societal learning from failures.

Oct 16, 2026

Decision models take off & Anthropic bans cruelty to Claude

The panel examines the rise of decision models, a family of LLM-based systems that return a single, scored classification in one forward pass, and asks whether they are genuinely new or a smarter use of existing architectures. It then weighs the cost and benefit of multi-agent teams, drawing on a study that found agent teams cost two to five times more without consistently better results, and closes with Anthropic's new policy against sustained cruelty toward its models and what it says about AI safety, ethics, and human behavior.

Oct 13, 2026

David Silver and Lasse Espeholt of Ineffable Intelligence: A Superlearner for the Age of Experience

David Silver, co-founder of Ineffable Intelligence, outlines his bold vision for building a "superlearner"—an AI that discovers science, mathematics, and language autonomously, free from human data or explicit guidance. Drawing parallels to AlphaGo Zero's success, Silver argues that human-language reasoning is a ceiling, not a foundation, for advanced AI. He details their approach using complex simulated "crucibles" and simple rewards, aiming to develop the "science of superintelligence" in simulation for safety and accelerated progress. With a powerful GPU cluster and a small, elite team, Silver forecasts strong evidence within a year and superintelligence within three.

Oct 13, 2026

The Section That Gives Away AI-Written Docs (with Carly Taylor)

Carly Taylor, a prominent engineering leader and founder of Rebel Data Science, discusses strategies for thriving amidst rapid AI advancements, the importance of authenticity and identifying "AI slop," balancing innovation with operational maintenance, and overcoming fear through embracing discomfort. She also addresses the "glass cliff" phenomenon for women in leadership and how to effectively leverage AI without sacrificing critical thinking.

Oct 05, 2026

Agentic Search vs Vector Search for Coding Agents: We Ran the Eval — Braintrust

This talk by Jess Wang from Braintrust delves into the critical importance of evaluations (evals) in AI development, moving beyond subjective "vibes" to data-driven decision-making. She breaks down the four core components of an eval—dataset, task, scoring, and experiments—and illustrates the continuous improvement "eval flywheel" integrating observability. The central experiment compares agentic search against vector search for a coding agent tasked with finding buggy code in Microsoft's TypeScript Go repo. While both methods achieved identical accuracy, vector search proved four times more expensive due to its inability to capture surrounding context within code chunks, leading to excessive, inefficient searches. Wang concludes with practical caveats and best practices for conducting effective LLM evals.

Sep 29, 2026

What Would Make an AI Assistant Worth Paying For?

This discussion with David Pawlan of Assistant Bench unpacks the explosive growth of personal AI agents, exploring their capabilities, current landscape, and future implications. Topics include diverse agent use cases from daily admin to finance, the evolution of human-agent interfaces (voice, hardware), the critical role of proactivity and trust, the emergence of silent multiplayer agents, and the transformative impact on commerce. The conversation also delves into agent-to-agent interactions, economic models, and the profound shift towards a consumer-aligned internet.